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course content

Зміст курсу

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate a Complete ML Pipeline

4. Modeling

ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

ML Introduction with scikit-learn

Creating a Pipeline ChallengeCreating a Pipeline Challenge

In this challenge, you need to put all the preprocessing steps we did together into one pipeline.
The dataset is the initial penguins.csv file we started from.
The first step is to remove two useless rows.
Then you will have to create a pipeline containing Encoding, Imputing, and Scaling.

You only need to encode two columns, 'sex' and 'island'. Since you do not want to encode the whole X, you must use ColumnTransformer. Then apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Завдання

  1. Import the correct function for making a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Set a remainder argument of make_column_transformer so that all the numerical columns remain untouched.
  4. Make a pipeline containing ct you just created, SimpleImputer with the strategy of 'most_frequent' and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Все було зрозуміло?

Секція 3. Розділ 4
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course content

Зміст курсу

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate a Complete ML Pipeline

4. Modeling

ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

ML Introduction with scikit-learn

Creating a Pipeline ChallengeCreating a Pipeline Challenge

In this challenge, you need to put all the preprocessing steps we did together into one pipeline.
The dataset is the initial penguins.csv file we started from.
The first step is to remove two useless rows.
Then you will have to create a pipeline containing Encoding, Imputing, and Scaling.

You only need to encode two columns, 'sex' and 'island'. Since you do not want to encode the whole X, you must use ColumnTransformer. Then apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Завдання

  1. Import the correct function for making a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Set a remainder argument of make_column_transformer so that all the numerical columns remain untouched.
  4. Make a pipeline containing ct you just created, SimpleImputer with the strategy of 'most_frequent' and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Все було зрозуміло?

Секція 3. Розділ 4
toggle bottom row
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